
#import dependencies
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
import os
import glob
import helpers
import matplotlib.dates as mdates
import plotly.express as px
import plotly.graph_objects as go
import re
import warnings
import nltk
nltk.download('stopwords')
from nltk.corpus import stopwords
warnings.filterwarnings('ignore')
from wordcloud import WordCloud
sb.set_style("darkgrid")
%matplotlib inline

[nltk_data] Downloading package stopwords to
[nltk_data]     C:\Users\20812015101195\AppData\Roaming\nltk_data...
[nltk_data]   Unzipping corpora\stopwords.zip.


# load data
df = pd.read_csv("cleaned_data/reddit_cleaned_2021.csv")
df_merged = pd.read_csv("cleaned_data/reddit_merged_2021.csv")

df_users = pd.read_csv("cleaned_data/users_cleaned.csv")


df.shape

(18305, 17)


df_merged.shape

(18305, 24)


# convert to datetime
df.created_at = pd.to_datetime(df.created_at)
df_merged.created_at = pd.to_datetime(df_merged.created_at)
df_merged.user_created_at = pd.to_datetime(df_merged.user_created_at)
df_users.user_created_at = pd.to_datetime(df_users.user_created_at)


# Filter the DataFrame on negative nltk and blob
df_negative = df.query(" sentiment_blob == sentiment_nltk == 'Negative' ") 
print(df_negative.shape)
df_negative.head(1);

(2016, 17)


# Filter the Merged DataFrame on negative nltk and blob
df_merged_negative = df_merged.query(" sentiment_blob == sentiment_nltk == 'Negative' ") 
print(df_merged_negative.shape)
df_merged_negative.head(1);

(2016, 24)


px.bar(data_frame=df_negative['top_level'].value_counts().to_frame().reset_index(),
       x="index", y="top_level").update_layout(title='Negative Comment or Submission (Top Level / Parent)',
                   xaxis_title='contribution top level / parent category',
                   yaxis_title='number of negative contributions').update_traces(marker_color='#5296dd')


px.bar(data_frame=df_negative['submission_comment'].value_counts().to_frame().reset_index(),
       x="index", y="submission_comment", color='submission_comment').update_layout(title='Comment or Submission (negative)',
                   xaxis_title='contribution category',
                   yaxis_title='number of negative contributions').update_traces(marker_color='#5296dd')


print('Unique classes of gathered models')
(df['sentiment_blob'] + ' ' + df['sentiment_nltk']).value_counts().to_frame().reset_index()

#.style.applymap(lambda x: helpers.coloring(x, {'Positive Positive': 'green', 'Negative Negative':'red', 
# 'Neutral Neutral': 'orange'}), subset=['index'])

Unique classes of gathered models


df_sentiment = df.query('sentiment_blob == sentiment_nltk')
print(df_sentiment.shape)
df_sentiment.head()

(9554, 17)


df_sentiment.drop(columns='sentiment_blob', inplace=True)
df_sentiment.rename(columns={'sentiment_nltk': 'sentiment'}, inplace=True)


print('Unified model data frame')
print(df_sentiment.shape)
df_sentiment.head(2) # .style.applymap(lambda x: helpers.coloring(x, {'Positive': '#deebce', 'Negative': '#edbcbb'}), subset=[ 'sentiment'])

Unified model data frame
(9554, 16)


common_df = df_sentiment.sentiment.value_counts().to_frame().reset_index()


common_df.columns = ['class', 'sentiment']


common_df


fig = px.histogram(data_frame=common_df,
                  x='class', y='sentiment',opacity=1, title='common results between nltk and blob ').update_traces(marker_color='#5296dd',)
fig.show()


fig = go.Figure()
fig.add_trace(go.Histogram(x=df.sentiment_blob, name='BLOB')) 

fig.add_trace(go.Histogram(x=df.sentiment_nltk, name= 'NLTK'))
# Overlay both histograms
# Reduce opacity to see both histograms
fig.update_traces(opacity=1)
fig.update_layout(
    title_text='BLOB VS NLTK', # title of plot
    xaxis_title_text='Class', # xaxis label
    yaxis_title_text='Number of contributions', # yaxis label
    bargap=0.2, # gap between bars of adjacent location coordinates
    bargroupgap=0.1 # gap between bars of the same location coordinates
)
fig.show()


temp_seniment_df = df['sentiment_nltk'].value_counts().to_frame().reset_index()
temp_seniment_df = temp_seniment_df.merge(df['sentiment_blob'].value_counts().to_frame().reset_index())
temp_seniment_df


explode = [0, 0, 0.1]

fig, (ax1, ax2) = plt.subplots(1, 2, figsize = (15,8))
ax1.pie(temp_seniment_df.sentiment_nltk,
        labels = temp_seniment_df.index,
        autopct='%1.1f%%',
        explode = explode,
        textprops={'fontsize': 12},
#         textprops={'color':"w"},
        colors = ['#adcde1', '#bcdd93', '#ee9e9c'],
        startangle=180)
ax2.pie(temp_seniment_df.sentiment_blob,
        labels = temp_seniment_df.index,
        colors = ['#adcde1', '#bcdd93', '#ee9e9c'],
        autopct='%1.1f%%',
        explode = explode,
        textprops={'fontsize': 12},
        startangle=180)
ax1.set_title('NLTK', fontdict = {'fontsize' : 18})
ax2.set_title('BLOB', fontdict = {'fontsize' : 18})
plt.show()


category_names = df_sentiment.index
dict_models = {
    'NLTK': temp_seniment_df.sentiment_nltk,
    'BLOB': temp_seniment_df.sentiment_blob
}


def models(dict_models, category_names):
    
    labels = list(dict_models.keys())
    data = np.array(list(dict_models.values()))
    data_cum = data.cumsum(axis=1)
    category_colors = plt.get_cmap('Paired')(
        np.linspace(0.08, 0.40, data.shape[1]))

    fig, ax = plt.subplots(figsize=(10, 5))
    ax.invert_yaxis()
    ax.xaxis.set_visible(False)
    ax.set_xlim(0, np.sum(data, axis=1).max())

    for i, (colname, color) in enumerate(zip(category_names, category_colors)):
        widths = data[:, i]
        starts = data_cum[:, i] - widths
        ax.barh(labels, widths, left=starts, height=0.5,
                label=colname, color=color)
        xcenters = starts + widths / 2

        r, g, b, _ = color
        text_color = 'white' if r * g * b < 0.25 else 'black'
        for y, (x, c) in enumerate(zip(xcenters, widths)):
            ax.text(x, y, str(int(c)), ha='center', va='center',
                    color=text_color)
    ax.legend(ncol=len(category_names), bbox_to_anchor=(0, 1),
              loc='lower left', fontsize=15)
    ax.tick_params(labelsize=14)
    sb.set_context('paper', font_scale=2) # --> just scaling all of the font sizes
    
    return fig, ax


models(dict_models, category_names)
plt.show()


# pd.set_option('display.max_colwidth', None)
suspected_dict = {}


df_fuc = df[df.text.str.lower().str.contains('fuck')]
print(df_fuc.shape)
df_fuc.head()

(1227, 17)


# get the authors of these submissions having the same submission text

mask = (df['submission_text'] == 'fuck_amber_heard') & (df['submission_comment'] == 'submission')
df_sub = df[mask]
print(df_sub.shape)

with pd.option_context('display.max_colwidth', None):
  display(df_sub.head())

(7, 17)


# get the authors of these submissions having the same submission text

mask = (df['submission_text'].str.contains('fuck')) & (df['submission_comment'] == 'submission')
df_sub_fuc = df[mask]
print(df_sub_fuc.shape)

with pd.option_context('display.max_colwidth', None):
  display(df_sub_fuc.head())

(49, 17)


df_sub_fuc_contributions = df_sub_fuc.groupby(df_sub_fuc.created_at.dt.date).size().reset_index(name='n_contributions')

fig = px.bar(df_sub_fuc_contributions,
             x='created_at', 
             y='n_contributions', title='The number of submissions with the word "F*CK" in 2021')
fig.update_traces(marker_color='red', marker_line_width=2, opacity=1, textposition='auto') 
# , marker_line_color='#5296dd'

fig.show()


df_fuc.author.value_counts().head(10)

-banned-                86
Jreal22                 10
Loveseeingthatsmile      6
AutoModerator            5
VampireQueenDespair      4
zephyrBoom72             4
EdwardCircumcizehand     4
blackweebow              4
Stanley_Elkind           4
WayneTedrowJunior        4
Name: author, dtype: int64


df_fuc.submission_comment.value_counts()

comment       1150
submission      77
Name: submission_comment, dtype: int64


df_fuc.subreddit.value_counts().head(10)

r/JerkOffToCelebs         188
r/entertainment           154
r/pussypassdenied         151
r/MensRights               82
r/TrueOffMyChest           66
r/iamatotalpieceofshit     61
r/DC_Cinematic             27
r/awfuleverything          27
r/CelebAssPussyMouth       27
r/AskReddit                26
Name: subreddit, dtype: int64


df_fuc.created_at.dt.date.value_counts().head(10)

2021-04-17    73
2021-02-28    72
2021-03-04    60
2021-05-11    45
2021-02-20    40
2021-01-16    34
2021-04-18    25
2021-05-08    25
2021-04-01    24
2021-01-01    23
Name: created_at, dtype: int64


df_fuc_contributions = df_fuc.groupby(df_fuc.created_at.dt.date).size().reset_index(name='n_contributions')

fig = px.bar(df_fuc_contributions,
             x='created_at', 
             y='n_contributions', title='The number of "F*CK" contributions in 2021')
fig.update_traces(marker_color='red', marker_line_width=2, opacity=1, textposition='auto') 
# , marker_line_color='#5296dd'

fig.show()


df_jeral = df.query(" author == 'Jreal22' ")
print(df_jeral.shape)
df_jeral.head()

(28, 17)


df_jeral.text.value_counts().head(3)

Fantastic beasts is shit anyways, plus what the fuck happened to JK Rowling lol. She went on some crusade to destroy trans people, it was like uhhh, we get it you're not a fan, but ya don't have to publish articles shitting on them.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             1
One thing I wish I could just get across in general to the people who just can't seem to put any blame on Amber is that there is a MASSIVE problem with women abusing men in America, that is just not talked about because women are who we hear about in the news getting beat up/killed by their male partners. We never hear about women beating men. \n\nSo there's this idea for people with no experience of it that women don't abuse men and it's just not the case. \n\nAnecdotal evidence is useless, I know, but in my life, I have witnessed more wives beating their husbands than ever men abusing their wives.\n\nAnd the guys have nowhere to go with the problem, they know that no one is going to believe that they can't control a 115 lb woman that is clawing at your face and smacking the shit out of you every night.\n\nThey don't want to talk to their friends about it, because then their friends think less of them or the friend tells their wife and the wife thinks the problem is the other way around and the woman is who is getting abused. \n\nWomen's problem with being abused is that when they're abused, they get badly hurt physically and we hear about it or we see it. \n\nBut when women abuse men, it's a shit load of psychological abuse and the physical abuse you aren't hearing about because men think it's their responsibility to "control" their wives, or they simply can't admit that their wives beat them, just like women have such a hard time telling friends/police that their boyfriends/husbands are abusing them.\n\nWomen abusing men is just not talked about in the news, and it's not shown in television and movies, so less people understand how just as terrible a problem it is. \n\nI guess because I watched my mom and friends of hers abuse their husbands so much growing up, I understand the other side of spousal abuse, and so when I see all these red flags from Amber, I recognize that the problem is clearly leaning towards her, while Depp is obviously a part of the problem as well.\n\nBut the history of women covering up black eyes with glasses and makeup is so commonly talked about and referenced in pop culture, the other side of that coin is just not talked about, and I think that's just disappointing, because I spent 20+ years of my life watching my mom abuse myself and my dad and then blame us when she had bruises on her body, or when she fell trying to hit my dad once and broke her wrist. \n\nWho did she blame when she broke her own wrist? My dad of course, who was trying to avoid being hit in the face again by her.\n\nThat's all I have to say on this stuff, I just want to get across the issue that I've seen throughout my life, and the reason I even say anything at all is because I see my own experience in Amber's abuse, and it's like she gets a free pass - doesn't lose movie roles like Depp, lies about donating the money Depp gave her to charity, and the charity even calls her out on it and still, no one cares. \n\nIt's just like, what does it take to get people to just admit that Amber is an abusive person, who has lied extensively, and has done massive damage to the metoo movement. \n\nIt makes me think, if my wife abused me, and I came out about it with literal video and audio proof of it, and still everyone blamed me and destroyed my life, how terrible would that be? That's what's happening to Depp, it's more complicated than that obviously, but I just imagine it's got to be nuts to be able to provide all this proof and yet people still give her a pass. \n\nIf they both were abusive, then punish them equally, that's all I'm asking here. \n\nWish everyone the best, I truly mean that.    1
"The actor told the High Court Ms Heard, 34, threw a vodka bottle at him which cut the top of his finger and "crushed the bones". \n\nThis was also confirmed by Amber on their recordings for their therapist that SHE was recording. \n\nAnd, I'm suddenly wrong because I've seen how a woman abuser gets away with being the main abuser in an abusive relationship? \n\nWith her having a previous relationship where her female partner admitted Amber beat her in an airport. \n\nYeah, maybe that means I know much more than you would about how men have to defend themselves from women like Amber that will literally chase you down (this was in the recorded court documents: she chased Johnny Depp into multiple rooms during a physical fight, where he and Heard both stated he ended up getting a different hotel room than her and additional security because she would not stop beating him, this was all recorded on HER tape).\n\nRead more, you'll look less like a moron defending an admitted abuser. She admitted on her own recording that she physically abused him and called him a "pussy" for running away from her during the fights.\n\nJust listen to one of Amber's recordings, it literally proves she's the abuser, without a shadow of a doubt. I'm not asking for much, listen to one damn recording. They're all on YouTube.\n\nI just can't imagine how anyone can listen to Amber sit there recording herself admitting to everything and still defend her.\n\nI don't care about Depp or Heard, but it drives me crazy that people can't just listen to Amber admit to everything that Johnny Depp has laid out.\n\nI'm not some asshole moron, I've simply seen this behavior first hand, and I've listened to all of Amber's recordings, which she recorded with Depp's permission to help in couple's therapy.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       1
Name: text, dtype: int64


suspected_dict['Jreal22'] = 'Created at:2019-06-23, used the word f*ck in 28 negative comments in APR 17,18 2021'


# Check for this user account datah
df_users.query(" user_name == 'Jreal22' ")


df_jeral_contributions = df_jeral.groupby(df_jeral.created_at.dt.date).size().reset_index(name='n_contributions')


fig = px.bar(df_jeral_contributions,
             x='created_at', 
             y='n_contributions', title='The number of "Amber Heard in Aquaman 2, why?" contributions in 2021')
fig.update_traces(marker_color='red', marker_line_width=2, opacity=1, textposition='auto') 
# , marker_line_color='#5296dd'

fig.show()


# Check the value counts of the negative text
df_negative.text.value_counts().head(15)

Fuck Amber Heard                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         9
Fuck amber heard                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         8
Amber Heard bad                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          8
Amber Heard Is Allegedly Being Investigated By LAPD For Perjury, Could Face Jail Time Over Johnny Depp Domestic Violence Accusations                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     7
**Reminder: Do not ask for personal information, suggest someone should be doxxed, link to or comment with personal information, openly solicit personal information, or contact the people featured here. Don't even wax poetic about wanting to post identifying information. You will be banned.**\n\n**Do not encourage, glorify, or incite violence.**\n\nFor example: "Kill yourself", "It wouldn't be so bad if we killed all the pedophiles", "This guy needs to die", "I hope this guy gets stabbed to death with a rusty screwdriver", etc.\n\nAll glorification, advocacy, or suggestions of violence, EVEN IN JEST, will be permanently banned, no exceptions, and no possibility of leniency. \n\n**ALL PERSONALLY IDENTIFYING INFORMATION MUST BE CENSORED.**\n\nFailure to follow the rules of this sub will result in a permanent ban.\n      \n\n\n*I am a bot, and this action was performed automatically. Please [contact the moderators of this subreddit](/message/compose/?to=/r/iamatotalpieceofshit) if you have any questions or concerns.*    5
Fuck Amber Heard.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        5
Fuck her                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 5
Amber heard bad                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          4
fuck amber heard                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         4
FBI TARGETS Amber Heard; CONFIRMS Australian FEDERAL CRIMINAL CASE!                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      4
Wtf                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      4
Fuck that bitch                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          4
amber heard bad                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          3
Cara Delevingne, Sommer Ray, Scarlett Johansson, Cardi B, Kaley Cuoco, and Amber Heard. 1: Cowgirl dressed as one of their characters, 2: Pile driver anal, 3: Public Airtight Gangbang, 4: Lapdance/Titfuck, 5: Brutal throatfuck, 6: Your own fuck method. Be as detailed as possible                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  3
downvote this comment if the meme sucks. upvote it and I'll go away.\n\n---\n\nr/dankmemescraft Minecraft server! Out now!                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               3
Name: text, dtype: int64


negative_words_count = df_negative.text.str.split(expand=True).stack().value_counts().to_frame().reset_index().rename(columns={'index': 'word', 0:'count'})


negative_words_count['word'] = negative_words_count['word'].apply(lambda x: x.lower())


negative_words_count['word'] = negative_words_count['word'].str.replace('[^\w\s]','')


stop_words = set(stopwords.words('english'))


temp_dict = negative_words_count[~negative_words_count.word.isin(stop_words)].set_index('count').to_dict()['word']
temp_dict;


frequency_dict = {value:key for (key,value) in temp_dict.items()}


wc = WordCloud(max_font_size=50, max_words=100, background_color="white").generate_from_frequencies(frequency_dict)
plt.figure(figsize=(15,10))
plt.imshow(wc)
plt.axis("off")
plt.show()


# this returns the number of submission_text which got the same parent(comment/submission)
df_negative.groupby('parent_id')['submission_text'].value_counts().sort_values(ascending=False)

parent_id   submission_text                                  
t3_msgoz8   johnny_depp_releases_lapd_bodycam_footage_proving    29
t3_lu5055   amber_heard_has_been_fired_from_jason_momoas         26
t3_lx2s7w   its_disgusting_that_people_are_less_angry_about      26
t3_n9lwnf   amber_heard_still_in_aquaman_2_despite_proof_that    25
t3_kyj460   amber_heard_is_a_monster_a_gold_digger_looking       18
                                                                 ..
t1_gp68n40  amber_heard_has_been_fired_from_jason_momoas          1
t1_gp652no  amber_heard_has_been_fired_from_jason_momoas          1
t1_gp64ygz  amber_heard_has_been_fired_from_jason_momoas          1
t1_gp63zi8  amber_heard_has_been_fired_from_jason_momoas          1
t1_gutfs2m  johnny_depp_releases_lapd_bodycam_footage_proving     1
Name: submission_text, Length: 1290, dtype: int64


df_same = df_negative.groupby('parent_id')['submission_text'].value_counts().to_frame()
df_same.columns = ['same_count']
df_same = df_same.reset_index()
df_same.sort_values('same_count', ascending=False, inplace=True)
df_same = df_same[df_same['parent_id'].str.startswith('t3')]
df_same


df_negative['text_words'].value_counts().head(10)

3     118
6      99
7      91
9      91
8      90
10     83
4      82
5      79
11     69
13     69
Name: text_words, dtype: int64


px.histogram(df_negative['text_words'].to_frame(), x="text_words",title='number of negative text words ',
            nbins=250).update_traces(marker_color='#5296dd')


# The number of words in negative submission text 

df_negative = df.query(" sentiment_blob == sentiment_nltk == 'Negative' ") #define again to add the "submission_words" column
df_negative_submission = df_negative.query(" submission_comment == 'submission' ") 

df_negative_submission['submission_words'].hist(figsize=(8,8),bins=30);
plt.title('The number of words in negative submission text');


df.author.value_counts().to_frame().head(10).reset_index()


fig = px.bar(df.author.value_counts().to_frame().head(10).reset_index(), x="author", y="index",
             height=500,
             title='Most commented user in 2021').update_traces(marker_color='#5296dd',).update_layout(
                   xaxis_title='number of comments',
                   yaxis_title='user name').update_traces(marker_color='#5296dd')

fig.update_yaxes(autorange="reversed")


df_auto_moderator = df.query(" author == 'AutoModerator' ").reset_index(drop=True)
print(df_auto_moderator.shape)
df_auto_moderator.head()

(515, 17)


df_auto_moderator.subreddit.value_counts().head(10)

r/JerkOffToCelebs         176
r/memes                    55
r/Celebhub                 48
r/unpopularopinion         32
r/AskReddit                30
r/DC_Cinematic             26
r/CelebBattleLeague        22
r/OutOfTheLoop             18
r/iamatotalpieceofshit     17
r/jerkbudss                 6
Name: subreddit, dtype: int64


df_auto_moderator['permalink'].iloc[26]

'/r/darkjokes/comments/kq9r9d/if_relationship_of_amber_heard_and_johnny_depp/gi2itdv/'


df_auto_moderator.text.value_counts().head()

### [Browse JerkOffChallenges](https://jerkofftocelebs.com/actors/)  • [Browse Picture Galleries](https://jerkofftocelebs.com/pictures/)  • NEW [JerkOffToGermanCelebs](https://reddit.com/r/JerkOffToGermanCelebs/)\n\n\n^(*Thank you for your submission. Make sure to follow the rules.*) \n\n^(*Check out our Website*) ^[*here*](https://jerkofftocelebs.com/). \n\n^(*Join our Discord*) ^[*here*](https://discord.gg/FMhrH2j).\n\n^(*Explore more subreddits*) ^[*here*](https://jerkofftocelebs.com/reddit-nsfw-list/).\n\n\n*I am a bot, and this action was performed automatically. Please [contact the moderators of this subreddit](/message/compose/?to=/r/JerkOffToCelebs) if you have any questions or concerns.*                                                                                          161
Rule 9 overused. No Johny Depp or Amber Heard memes at this time\n\n*I am a bot, and this action was performed automatically. Please [contact the moderators of this subreddit](/message/compose/?to=/r/memes) if you have any questions or concerns.*                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      52
Make sure to follow our community guidelines.\n\n**[Get user flair](https://redd.it/e0a4mn) and check out our [Instagram page](https://www.instagram.com/celebtag) for more celebrity content!**\n\n\n*I am a bot, and this action was performed automatically. Please [contact the moderators of this subreddit](/message/compose/?to=/r/Celebhub) if you have any questions or concerns.*                                                                                                                                                                                                                                                                                                                                                                                                                                 42
Your submission has been automatically removed for not including a valid category/subcategory tag. Tags are essential to an optimal browsing experience for our users.\n\nSince your post was removed automatically, you are free to resubmit it with an appropriate tag. You can find the tagging guide [here](/r/DC_Cinematic/wiki/linkflair#wiki_automated_tagging). Add a valid and appropriate tag in your submission title. Choose wisely, as posts with misleading tags are subject to removal.\n\n**Message the moderators if your post was removed despite being tagged with an input from the category list.**\n\n\n*I am a bot, and this action was performed automatically. Please [contact the moderators of this subreddit](/message/compose/?to=/r/DC_Cinematic) if you have any questions or concerns.*     26
Your opinion about celebrity relationships has been reposted dozens of times today. Please join a celebrity gossip sub. Thanks.\n\n*I am a bot, and this action was performed automatically. Please [contact the moderators of this subreddit](/message/compose/?to=/r/unpopularopinion) if you have any questions or concerns.*                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            24
Name: text, dtype: int64


df_vote_bot = df.query(" author == 'CelebBattleVoteBot' ").reset_index(drop=True)
print(df_vote_bot.shape)
df_vote_bot.head()

(163, 17)


df_vote_bot['permalink'].iloc[0]

'/r/CelebbattlePolls/comments/ko1ew0/marvel_vs_dc_team_marvel_evangeline_lilly_brie/'


df_vote_bot.subreddit.value_counts().head(10)

r/CelebbattlePolls    83
r/CelebBattles        79
r/JerkOffToCelebs      1
Name: subreddit, dtype: int64


df_vote_bot.text.value_counts().head()

Amanda Seyfried VS. Amber Heard                                                                                                                       2
Anne Hathaway vs Amber Heard                                                                                                                          2
Hottest League : Kristen Bell VS Amber Heard                                                                                                          1
Poll for [Amber Heard vs Torrie Wilson](https://reddit.com/lp7af8) on CelebBattles                                                                    1
Vote here: https://new.reddit.com/r/CelebbattlePolls/comments/mm3k4p\n\n---\n\n^^I'm ^^a ^^bot. ^^This ^^action ^^was ^^performed ^^automatically.    1
Name: text, dtype: int64


df_charliedba = df.query(" author == 'charliedba' ").reset_index(drop=True)
print(df_charliedba.shape)
df_charliedba.head()

(99, 17)


df_charliedba['permalink'].iloc[0]

'/r/Amber_Heard/comments/ms8xkq/long_legged_beauty_amber_heard/'


df_charliedba.subreddit.value_counts().head(10)

r/Amber_Heard_2       80
r/Amber_Heard          7
r/HighResCelebs        3
r/UHQcelebs            3
r/BeautifulFemales     2
r/PrettyWomen          1
r/FamousFaces          1
r/Celebs               1
r/Celebhub             1
Name: subreddit, dtype: int64


df_charliedba.text.value_counts().head(10)

Amber Heard                      31
Stunning Amber Heard              4
Gorgeous Amber Heard              3
Angelic Amber Heard               3
Cute Amber Heard                  3
Amber Heard [2100 x 3150]         3
Young Amber Heard                 2
Amber Heard [High Resolution]     2
Amber Heard [2550 x 3645]         2
Amber Heard [2218 x 3000]         2
Name: text, dtype: int64


# check the date this account was creted

# user_name --> index
# at[index_value , 'column']
# df_users.set_index('user_name').at['Stanley_Elkind', 'user_created_at']

df_users[df_users.user_name == 'Stanley_Elkind']


df_stanley = df.query(" author == 'Stanley_Elkind' ").reset_index(drop=True)
print(df_stanley.shape)
df_stanley.head()

(44, 17)


df_stanley.subreddit.value_counts().head(10)

r/JerkOffToCelebs        36
r/pickoneceleb            3
r/CelebAssPussyMouth      2
r/CelebWouldYouRather     2
r/DC_Cinematic            1
Name: subreddit, dtype: int64


df_stanley.text.value_counts().head(10)

Breed repeatedly on my own: Gal\n\nBreed once: Melissa\n\nGang bang Margot\n\nAnd this was obviously just an excuse to hear other people say theyd watch Amber Heard get fucked by a horse                                                                                                  1
She’s going to need it. We won’t be gentle.                                                                                                                                                                                                                                                 1
Oral slaves for a month: Margot and Elizabeth\n\nOne time BDSM session: Scarlett (dom) and Amber (sub)\n\nLesbian sex while I watch and jerk off: Gal and Brie                                                                                                                              1
When it comes to Amber Heard I let my penis make the decisions.                                                                                                                                                                                                                             1
Rough? DC\n\nPassionate, deep penetrating sex is for Marvel                                                                                                                                                                                                                                 1
Her ass is so nice 🤤                                                                                                                                                                                                                                                                        1
She knows the way to a man’s soul                                                                                                                                                                                                                                                           1
Everyone is cool burgeoning cult leader Jared Leto though.                                                                                                                                                                                                                                  1
u r goin to jail 4 lyfe                                                                                                                                                                                                                                                                     1
Pick one to wake up next to, fool around with a bit and make love to well into the afternoon and another to show up to your house at night to be your utterly compliant sex toy until dawn - Elizabeth Olsen, Emilia Clarke, Ana de Armas, Jennifer Lawrence, Amber Heard, Margot Robbie    1
Name: text, dtype: int64


df_stanley_contributions = df_stanley.groupby(df_stanley.created_at.dt.date).size().reset_index(name='n_contributions')


fig = px.bar(df_stanley_contributions,
             x='created_at', 
             y='n_contributions', title='The number of "Truthbetheprejudice" contributions in 2021')
fig.update_traces(marker_color='red', marker_line_width=2, opacity=1, textposition='auto') 
# , marker_line_color='#5296dd'

fig.show()


# check the date this account was creted
df_users[df_users.user_name == 'Truthbetheprejudice']


df_truth = df.query(" author == 'Truthbetheprejudice' ").reset_index(drop=True)
print(df_truth.shape)
df_truth.head()

(43, 17)


df_truth.subreddit.value_counts().head(10)

r/MensRights              10
r/movies                  10
r/entertainment           10
r/pussypassdenied          8
r/JusticeForJohnnyDepp     5
Name: subreddit, dtype: int64


df_truth.text.value_counts().head(10)

Amber Heard Is Allegedly Being Investigated By LAPD For Perjury, Could Face Jail Time Over Johnny Depp Domestic Violence Accusations    5
Amber Heard Under Investigation for Perjury in Johnny Depp Domestic Violence Case                                                       5
Amber Heard probed for perjury and staging 2016 Johnny Depp domestic violence case: 'Lock her up'                                       5
Amber Heard to struggle in new LAPD domestic violence probe                                                                             5
Johnny Depp sues ACLU on to see if ex-wife Amber Heard gave $7m divorce settlement                                                      5
#JusticeForJohnnyDepp Johnny Depp fans donate $40k to children that Amber Heard neglected                                               4
Remove Amber Heard from Aquaman 2                                                                                                       4
Confirmed: Amber Heard Under Criminal Investigation in Australia                                                                        4
Big tech silence Johnny Depp and censor Amber Heard evidence                                                                            3
Johnny Depp Makes an Appearance in Spain as Lawyers Drop New Evidence in Amber Heard Case                                               2
Name: text, dtype: int64


df_truth_contributions = df_truth.groupby(df_truth.created_at.dt.date).size().reset_index(name='n_contributions')
df_truth_contributions


fig = px.bar(df_truth_contributions,
             x='created_at', 
             y='n_contributions', title='The number of "Truthbetheprejudice" contributions in 2021')
fig.update_traces(marker_color='red',
                  marker_line_width=2, opacity=1, textposition='auto')
fig.show()


df_gaul = df.query(" author == 'gaul66' ").reset_index(drop=True)
print(df_gaul.shape)
df_gaul.head()

(37, 17)


df_gaul.subreddit.value_counts().head(10)

r/CelebBattles    37
Name: subreddit, dtype: int64


df_gaul['permalink'].iloc[5]

'/r/CelebBattles/comments/l4h1wp/amber_heard_vs_brie_larson/gkpl5eg/'


# check the date this account was creted
df_users[df_users.user_name == 'sadwook']


df_sadwook = df.query(" author == 'sadwook' ").reset_index(drop=True)
print(df_sadwook.shape)
df_sadwook.head()

(32, 17)


df_sadwook.subreddit.value_counts().head(10)

r/pussypassdenied    32
Name: subreddit, dtype: int64


df_sadwook_contributions = df_sadwook.groupby(df_sadwook.created_at.dt.date).size().reset_index(name='n_contributions')
df_sadwook_contributions


fig = px.bar(df_sadwook_contributions,
             x='created_at', 
             y='n_contributions', title='The number of "sadwook" contributions in 2021')
fig.update_traces(marker_color='red',
                  marker_line_width=2, opacity=1, textposition='auto')
fig.show()


df_sadwook_hrs = df_sadwook.groupby(df_sadwook.created_at.dt.hour).size().reset_index(name='n_contributions')
df_sadwook_hrs


fig = px.bar(df_sadwook_hrs,
             x='created_at', 
             y='n_contributions', title='The number of "sadwook" contributions in 2021')
fig.update_traces(marker_color='red', 
                  marker_line_width=2, opacity=1, textposition='auto')
fig.show()


df_beatplayer = df.query(" author == 'Beatplayer' ").reset_index(drop=True)
print(df_beatplayer.shape)
df_beatplayer.head()

(32, 17)


df_beatplayer['permalink'].iloc[0]

'/r/TrueOffMyChest/comments/lx2s7w/its_disgusting_that_people_are_less_angry_about/gpl1ocz/'


df_beatplayer.subreddit.value_counts().head(10)

r/TrueOffMyChest    32
Name: subreddit, dtype: int64


df_beatplayer.text.value_counts().head(3)

I’m sure that I’d rake your subjective interpretation of the body language of a DV violence victim in court of video evidence, and I think that’s the crux of the issue with women being beloved tbh.                              1
Wait wait. Has something changed here. Because she won two cases in jurisdictions that are notoriously difficult to have female evidence believed. \n\nShe had videos.\n\nGenuine ask - am I missing this evidence of AH lying?    1
I mean - neither of those situations are rape. They’re absolutely gross, and criminal, but they are absolutely not rape. \n\nCheck it out and get back to me with a retraction.                                                    1
Name: text, dtype: int64


df_negative.author.value_counts()

-banned-                130
AutoModerator             9
LoveAmberHeard42286       9
Stanley_Elkind            9
KeepingDankMemesDank      8
                       ... 
officialnast              1
I_bless_you               1
AnnaBortion269            1
TacticalSnacks            1
IamHere019                1
Name: author, Length: 1620, dtype: int64


fig = px.bar(df_negative.author.value_counts().to_frame().head(10).reset_index(), x="author", y="index",
             height=500,
             title='Authors with most Negative Comments / Submissions').update_traces(marker_color='#5296dd',).update_layout(
                   xaxis_title='number of comments / submissions',
                   yaxis_title='user name').update_traces(marker_color='#5296dd')

fig.update_yaxes(autorange="reversed")


# suspected3 = df_negative.author.value_counts().head(10).index.to_list()

# set(suspected3) & set(suspected_list)


# Check for the dates these accounts where created


# suspected_list = suspected_list + suspected3


# # Furthe investigate the text of the most negative commented users

# df_text5 = df[df.author.isin(suspected3)]
# df_text5 = df_text5.sort_values('created_at')

# print(df_text5.shape)
# df_text5.head(60)


# df_text5.text.value_counts().head(10)


df_negative_submission = df_negative[df_negative.submission_comment == 'submission']
print(df_negative_submission.shape)
df_negative_submission.head(2)

(167, 17)


df_negative_submission.author.value_counts().nlargest(n=10)

-banned-               60
Truthbetheprejudice     5
bradje61                3
Sleeper1034             3
krttz35                 3
DerpimusNoobimus        2
Memestrats4life         2
shalanarose             2
MikiSayaka33            2
spiritual_one1          2
Name: author, dtype: int64


df_negative_submission.author.value_counts().to_frame().head(10)


fig = px.bar(df_negative_submission.author.value_counts().to_frame().head(10).reset_index(), x="author", y="index",
             height=500,
             title='Authors with most Negative Submissions').update_traces(marker_color='#5296dd',).update_layout(
                   xaxis_title='Number of Negative Submissions',
                   yaxis_title='Author_Name').update_traces(marker_color='#5296dd')

fig.update_yaxes(autorange="reversed")


# suspected4 = df_negative_submission.author.value_counts().nlargest(n=10).index.to_list()
# set(suspected4) & set(suspected_list)


# suspected_list = suspected_list + suspected4


# # Furthe investigate the text of the most negative submitted users

# df_text6 = df[df.author.isin(suspected4)]
# df_text6 = df_text6.sort_values('created_at')

# print(df_text6.shape)
# df_text6.head()


# df_text6.text.value_counts().head(10)


df_negative.author.value_counts().nlargest(n=25)

-banned-                130
AutoModerator             9
LoveAmberHeard42286       9
Stanley_Elkind            9
KeepingDankMemesDank      8
my_alt_account1312        6
DrewFlan                  6
Beatplayer                5
Truthbetheprejudice       5
AltruisticVariation4      5
The_Scamp                 5
durant92bhd               4
modsRwads                 4
Juju_mila                 4
Zom-bom                   4
amphibiousParakeet        4
flat_earth_pancakes       4
Anonymous2401             4
nojodricri                4
Furkan38000               4
WayneTedrowJunior         4
WhosDadIsThat             3
reptilicious1             3
Reddit_Shadow_            3
DerpimusNoobimus          3
Name: author, dtype: int64


check_list = df_negative.author.value_counts().nlargest(n=25).index.tolist()[1:]
check_list

['AutoModerator',
 'LoveAmberHeard42286',
 'Stanley_Elkind',
 'KeepingDankMemesDank',
 'my_alt_account1312',
 'DrewFlan',
 'Beatplayer',
 'Truthbetheprejudice',
 'AltruisticVariation4',
 'The_Scamp',
 'durant92bhd',
 'modsRwads',
 'Juju_mila',
 'Zom-bom',
 'amphibiousParakeet',
 'flat_earth_pancakes',
 'Anonymous2401',
 'nojodricri',
 'Furkan38000',
 'WayneTedrowJunior',
 'WhosDadIsThat',
 'reptilicious1',
 'Reddit_Shadow_',
 'DerpimusNoobimus']


# get a data frame with the most negative-comments users
df_check = df_users[df_users['user_name'].isin(check_list)]
print(df_check.shape)
df_check.head(2)

(24, 10)


df_check['user_name'].nunique()

24


for col in df_check.columns:
    if col not in ['user_name', 'user_created_at']:
        print('The value counts of the users contributing the most to negative comments/submissions: ' + col)
        print(df_check[col].value_counts())
        print('\n')

The value counts of the users contributing the most to negative comments/submissions: has_verified_email
True     21
False     3
Name: has_verified_email, dtype: int64


The value counts of the users contributing the most to negative comments/submissions: is_mod
False    16
True      8
Name: is_mod, dtype: int64


The value counts of the users contributing the most to negative comments/submissions: is_gold
False    18
True      6
Name: is_gold, dtype: int64


The value counts of the users contributing the most to negative comments/submissions: is_banned
False    20
True      4
Name: is_banned, dtype: int64


The value counts of the users contributing the most to negative comments/submissions: comment_karma
14105.0     1
146.0       1
1000.0      1
12123.0     1
90873.0     1
49687.0     1
12964.0     1
13977.0     1
188.0       1
4174.0      1
15574.0     1
1916.0      1
110672.0    1
18790.0     1
325044.0    1
22104.0     1
5880.0      1
1206.0      1
30348.0     1
402308.0    1
Name: comment_karma, dtype: int64


The value counts of the users contributing the most to negative comments/submissions: link_karma
3388.0     1
1372.0     1
1000.0     1
15274.0    1
1127.0     1
5513.0     1
14009.0    1
10143.0    1
2680.0     1
9269.0     1
345.0      1
1185.0     1
3104.0     1
2985.0     1
51.0       1
10931.0    1
30001.0    1
20.0       1
1788.0     1
1.0        1
Name: link_karma, dtype: int64


The value counts of the users contributing the most to negative comments/submissions: banned_unverified
others        17
banned         4
unverified     3
Name: banned_unverified, dtype: int64


The value counts of the users contributing the most to negative comments/submissions: creation_year
2020      7
others    6
banned    4
2021      3
2019      2
2018      2
Name: creation_year, dtype: int64


df_negative.subreddit.value_counts()

r/entertainment           287
r/pussypassdenied         204
r/JerkOffToCelebs         173
r/MensRights              134
r/iamatotalpieceofshit    114
                         ... 
r/HolUp                     1
r/Celebswithbigtits         1
r/agedlikewine              1
r/TIHI                      1
r/celebheels                1
Name: subreddit, Length: 176, dtype: int64


df_negative.subreddit.value_counts().nlargest(n=25)

r/entertainment            287
r/pussypassdenied          204
r/JerkOffToCelebs          173
r/MensRights               134
r/iamatotalpieceofshit     114
r/AskReddit                 87
r/TrueOffMyChest            86
r/DC_Cinematic              74
r/EntitledBitch             45
r/movies                    40
r/redditmoment              38
r/awfuleverything           33
r/PurplePillDebate          32
r/PrequelMemes              30
r/celebnsfw                 29
r/CelebBattles              29
r/dankmemes                 20
r/JusticeForJohnnyDepp      20
r/memes                     18
r/TheStand                  17
r/SubredditDrama            17
r/Makeup                    15
r/funny                     14
r/dontputyourdickinthat     14
r/gameofthrones             13
Name: subreddit, dtype: int64


fig = px.bar(df_negative.subreddit.value_counts().to_frame().head(25).reset_index(), x="subreddit", y="index",
             height=500,
             title='Subreddits with most Negative Comments / Submissions').update_traces(marker_color='#5296dd',).update_layout(
                   xaxis_title='Number of Negative Comments',
                   yaxis_title='subbredit').update_traces(marker_color='#5296dd')

fig.update_yaxes(autorange="reversed")


df['urls'].nunique()

233


df[df.astype(str)['urls'] != '[]'].head(2)


df['urls'].astype('str').value_counts().head()

[]                                                                                                                                                                          17522
['https://jerkofftocelebs.com', 'https://jerkofftocelebs.com', 'https://reddit.com', 'https://jerkofftocelebs.com', 'https://discord.gg', 'https://jerkofftocelebs.com']      161
['https://www.reddit.com']                                                                                                                                                     49
['https://reddit.com']                                                                                                                                                         43
['https://redd.it', 'https://www.instagram.com']                                                                                                                               42
Name: urls, dtype: int64


# the value counts of the # of urls
df['urls_count'].value_counts()

0     17522
1       394
6       170
2       130
3        41
5        19
4        14
8         5
9         3
10        3
16        2
20        1
60        1
Name: urls_count, dtype: int64


df['urls_count'].value_counts()[1:].plot(kind='bar', figsize=(8,8));
plt.title('Count of the number of URLS in each submission/comment');


df_negative.parent_id.value_counts().nlargest(n=25)

t3_msgoz8    29
t3_lu5055    26
t3_lx2s7w    26
t3_n9lwnf    25
t3_kyj460    18
t3_lgbi6k    15
t3_mgjuv3    15
t3_n77qe8    12
t3_lo748q    12
t3_mssbu1    10
t3_l6ler4    10
t3_n7ug34    10
t3_ko1xfy     9
t3_l8g47j     8
t3_ktkm8u     8
t3_n884fx     8
t3_kqbj53     8
t3_mygtjf     7
t3_kr5da2     7
t3_kxwb32     7
t3_msz0b7     6
t3_ncf0uq     6
t3_l898tv     6
t3_lo6f92     6
t3_n95rfx     6
Name: parent_id, dtype: int64


fig = px.bar(df_negative.parent_id.value_counts().to_frame().head(25).reset_index(), x="parent_id", y="index",
             height=500,
             title='sumbissions with most Negative comments').update_traces(marker_color='#5296dd',).update_layout(
                   xaxis_title='Number of Negative Comments',
                   yaxis_title='subbredit').update_traces(marker_color='#5296dd')

fig.update_yaxes(autorange="reversed")


# note that value_counts() neglect Zeros
df_merged_negative["days_after_creation"].value_counts()

632.0     8
358.0     7
67.0      7
150.0     6
3355.0    5
         ..
1355.0    1
2531.0    1
763.0     1
1096.0    1
1211.0    1
Name: days_after_creation, Length: 1225, dtype: int64


px.histogram(df_merged_negative, x="days_after_creation",title='days_after_creation',
            nbins=250).update_traces(marker_color='#5296dd',).update_layout(
                   xaxis_title='number of days',)


print('The number of accounts posted the same day they was created!')
df_merged_negative[df_merged_negative['days_after_creation'] == 0].shape[0]

The number of accounts posted the same day they was created!

3


print('The number of accounts posted the same week they was created!')
df_merged_negative[df_merged_negative['days_after_creation'] <= 7].shape[0]

The number of accounts posted the same week they was created!

21


print('The number of accounts posted the same month they was created!')
df_merged_negative[df_merged_negative['days_after_creation'] <= 30].shape[0]

The number of accounts posted the same month they was created!

50


df_merged_negative[df_merged_negative['days_after_creation'] <= 30]['user_created_at'].dt.year.value_counts()

2021    35
2020    15
Name: user_created_at, dtype: int64


mask = (df_merged_negative['days_after_creation'] <= 30) & (df_merged_negative['user_created_at'].dt.year == 2021)
df_merged_negative[mask]['user_created_at'].dt.strftime('%b').value_counts()

Jan    11
Apr     9
Feb     7
Mar     7
May     1
Name: user_created_at, dtype: int64


months = df_merged_negative[df_merged_negative['days_after_creation'] <= 30]['user_created_at'].dt.strftime('%b')
months_sorted = months.value_counts()[['Jan', 'Feb', 'Mar', 'Apr', 'May']]
months_sorted

Jan    11
Feb     7
Mar     7
Apr     9
May     1
Name: user_created_at, dtype: int64


months_sorted.plot(kind='bar', figsize=(8,8), title='contributions of the accounts negatively posted/commented \n the same month they were created');


# THE SAME MONTH:
# check for the date these accounts posted/commented 
reddit_30 = df_merged_negative[df_merged_negative['days_after_creation'] <= 30]

dates_count = df_merged_negative.groupby(reddit_30['created_at'].dt.date).size().reset_index(name='contributions')
dates_count.sort_values('contributions', ascending=False);


fig = px.bar(dates_count,
             x='created_at', 
             y='contributions', title = 'contributions of the accounts posted/commented the same month they were created')
fig.update_traces(marker_color='#5296dd',
                  marker_line_width=1.5, opacity=1, textposition='auto').update_layout()
fig.show()


# THE SAME WEEK
# check for the date these accounts posted/commented
reddit_7 = df_merged_negative[df_merged_negative['days_after_creation'] <= 7]

dates_count_7 = df_merged_negative.groupby(reddit_7['created_at'].dt.date).size().reset_index(name='contributions')
dates_count_7.sort_values('contributions', ascending=False);


fig = px.bar(dates_count_7,
             x='created_at', 
             y='contributions', title = 'contributions of the accounts posted/commented the same week they were created')
fig.update_traces(marker_color='#5296dd',
                  marker_line_width=1.5, opacity=1, textposition='auto').update_layout()
fig.show()


# THE SAME DAY
# check for the date these accounts posted/commented
reddit_1 = df_merged_negative[df_merged_negative['days_after_creation'] <= 0]

dates_count_1 = df_merged_negative.groupby(reddit_1['created_at'].dt.date).size().reset_index(name='contributions')
dates_count_1.sort_values('contributions', ascending=False);


fig = px.bar(dates_count_1,
             x='created_at', 
             y='contributions', title = 'contributions of the accounts posted/commented the same day they were created')
fig.update_traces(marker_color='#5296dd',
                  marker_line_width=1.5, opacity=1, textposition='auto').update_layout()
fig.show()


# get the author names that commented in a negative way the same month the account was created
# to add to the suspected list
df_merged_30 = df_merged_negative.query("days_after_creation <= 30 & sentiment_blob == sentiment_nltk == 'Negative' ")
df_merged_30.head()


# suspected5 = (df_merged_30.user_name).tolist()

# set(suspected5) & set(suspected_list)


# suspected_list = suspected_list + suspected5


# with pd.option_context('display.max_colwidth', None):
#   display(df_merged.query("user_name == 'LoveAmberHeard42286' ").text.head())


# suspected_list.remove('LoveAmberHeard42286')


# len(suspected_list)


# the negative contributions of user accounts created in each year
negative_contributions = df_merged_negative.groupby(df_merged_negative['user_created_at'].dt.year).size().reset_index(name='n_accounts')
negative_contributions


# , text='n_accounts' --> this displays the Y-Axis values on the bars
fig = px.bar(negative_contributions,
             x='user_created_at', y='n_accounts', text='n_accounts', title='Number of User Accounts Created in each year / having negative contributions in 2021')
fig.update_traces(marker_color='#5296dd',
                  marker_line_width=1.5, opacity=1, textposition='auto')

fig.show()


# grop by date and count
trendy_dates_negative = df_negative.groupby(df_negative['created_at'].dt.date).agg('count')['created_at'].to_frame()


# naming the count column as contribution_count
trendy_dates_negative.columns = ['contribution_count']
trendy_dates_negative.sort_values('contribution_count', ascending=False, inplace=True)

trendy_dates_negative = trendy_dates_negative.reset_index()


fig = px.bar(trendy_dates_negative,
             x='created_at', y='contribution_count')

fig.update_layout(
            title={
        'text': "The number of negative contributions created in each date",
        'x':0.5,
        'xanchor': 'center',
        'yanchor': 'top'
        })

fig.update_traces(marker_color='#5296dd',
                  marker_line_width=1.5, opacity=1, textposition='auto').update_layout()
fig.show()


# get the top 10 trendy dates first, then sort them by date
top_trendy_negative = trendy_dates_negative.head(10)
top_trendy_negative.sort_values('created_at', inplace=True)


top_trendy_negative = top_trendy_negative.head(10).reset_index()


fig = px.bar(top_trendy_negative, x="created_at", y="contribution_count",
             height=500,
             title='Highest negative contrbutions in 2021').update_traces(marker_color='#5296dd',).update_layout(
                   xaxis_title='Date',
                   yaxis_title='Number of negative contrbutions').update_traces(marker_color='#5296dd')

fig.update_layout(
    xaxis = dict(
        title='Contribution Date',
        tickmode = 'array',
        tickvals = top_trendy_negative.created_at,
    )
)


# check for the accounts contributed on the peak day (Apr 17,2021)
df_merged_negative[df_merged_negative.created_at.dt.strftime('%Y-%m-%d') == '2021-04-17']\
.user_created_at.dt.year.value_counts().sort_index().plot(kind='bar', figsize=(13,8));

# plt.gca().invert_xaxis()
plt.title('The creation year of the accounts contributed on the peak day (Apr 17,2021)');
plt.xlabel('Accout Creation Year');
plt.ylabel('n_contributions');


# check for the accounts contributed on the peak day (Feb 20,2021)
df_merged_negative[df_merged_negative.created_at.dt.strftime('%Y-%m-%d') == '2021-02-20']\
.user_created_at.dt.year.value_counts().sort_index().plot(kind='bar', figsize=(13,8));

# plt.gca().invert_xaxis()
plt.title('The creation year of the accounts contributed on the peak day (Feb 20,2021)');
plt.xlabel('Accout Creation Year');
plt.ylabel('n_contributions');


# check for the accounts contributed on the peak day (Feb 28,2021)
df_merged_negative[df_merged_negative.created_at.dt.strftime('%Y-%m-%d') == '2021-02-28']\
.user_created_at.dt.year.value_counts().sort_index().plot(kind='bar', figsize=(13,8));

# plt.gca().invert_xaxis()
plt.title('The creation year of the accounts contributed on the peak day (Feb 28,2021)');
plt.xlabel('Accout Creation Year');
plt.ylabel('n_contributions');


# check for the hour these negative contributions were made
df_hours_negative = df_negative.groupby(df_negative['created_at'].dt.hour).size().reset_index(name='contribution_count')
df_hours_negative.sort_values('contribution_count', ascending=False);


fig = px.bar(df_hours_negative,
             x='created_at', y='contribution_count', 
             title='Number of contrbutions Comment/Submission in day hours')

fig.update_layout(
    xaxis = dict(
        title='Hours of Day',
        tickmode = 'linear',
        dtick = 1
    )
)

fig.update_traces(marker_color='#5296dd',
                  marker_line_width=1.5, opacity=1, textposition='auto').update_layout()
fig.show()


week_day_negative = df_negative['created_at'].dt.strftime('%a')
# one can sort by any order by providing a custom index explicitely :
# https://stackoverflow.com/questions/43855474/changing-sort-in-value-counts/43855492
week_day_negative_sorted = week_day_negative.value_counts()[['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']]
week_day_negative_sorted

Mon    252
Tue    298
Wed    190
Thu    232
Fri    206
Sat    516
Sun    322
Name: created_at, dtype: int64


week_day_negative_sorted.plot(kind='bar', color='#5296dd', figsize = (8,8), title='In Which days users created more negative?');
# plt.gca().invert_xaxis()


# # ! --> used to run terminal/ command line expression in jupyter notebook
# !jupyter nbconvert New_2021_Analysis.ipynb --TagRemovePreprocessor.remove_input_tags='{"remove_cell"}' --to slides -- no-input


# # downgraded nbconvert to 5.6 for this code to be able to use the output_toggle (hide code cells)
# !jupyter nbconvert New_2021_Analysis.ipynb --to slides --post serve --template output_toggle

	index	0
0	Neutral Neutral	4862
1	Positive Neutral	3318
2	Positive Positive	2676
3	Negative Neutral	2191
4	Negative Negative	2016
5	Neutral Negative	1266
6	Neutral Positive	906
7	Positive Negative	712
8	Negative Positive	358

	created_at	n_contributions
0	2021-04-18	3
1	2021-04-20	4
2	2021-04-29	4
3	2021-04-30	4
4	2021-05-07	8
5	2021-05-08	5
6	2021-05-09	5
7	2021-05-10	5
8	2021-05-19	5

	user_created_at	n_accounts
0	2006.0	5
1	2007.0	3
2	2008.0	8
3	2009.0	7
4	2010.0	16
5	2011.0	164
6	2012.0	73
7	2013.0	65
8	2014.0	69
9	2015.0	82
10	2016.0	127
11	2017.0	133
12	2018.0	212
13	2019.0	317
14	2020.0	446
15	2021.0	129

	child_id	permalink	text	parent_id	subreddit	author	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	text_words	submission_text	submission_words	urls
4	t1_ghnpthf	/r/CelebBattles/comments/ko1duk/marvel_vs_dc_t...	10/10 on layout and choices. Very good, op. \n...	t3_ko1duk	r/CelebBattles	Caped_Baldy91	2021-01-01 00:30:16	Positive	Positive	16.0	submission	comment	13	marvel_vs_dc_team_marvel_evangeline_lilly_brie	8	[]
5	t1_ghnr4bt	/r/WouldYouRather/comments/knz2cw/would_you_ra...	r/redditmoment	t3_knz2cw	r/WouldYouRather	SlavShinigamii	2021-01-01 00:42:44	Neutral	Neutral	3.0	submission	comment	1	would_you_rather_slap_amber_heard_until_she	8	[]
8	t3_ko2lp6	/r/trendandstyle/comments/ko2lp6/the_stands_am...	The Stand's Amber Heard Sheds Light on Nadine'...	NaN	r/trendandstyle	templederr	2021-01-01 01:39:37	Positive	Positive	1.0	NaN	submission	12	the_stands_amber_heard_sheds_light_on_nadines	8	[]
9	t1_ghnymy7	/r/CelebBattles/comments/ko1duk/marvel_vs_dc_t...	DC	t3_ko1duk	r/CelebBattles	masseffect2001	2021-01-01 01:57:50	Neutral	Neutral	3.0	submission	comment	1	marvel_vs_dc_team_marvel_evangeline_lilly_brie	8	[]
10	t3_ko33pc	/r/WouldYouRather/comments/ko33pc/wyr_savagely...	WYR savagely beat up Amber Heard or save Johnn...	NaN	r/WouldYouRather	canadianreject565	2021-01-01 02:12:34	Neutral	Neutral	4.0	NaN	submission	11	wyr_savagely_beat_up_amber_heard_or_save_johnny	9	[]

	child_id	permalink	text	parent_id	subreddit	author	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	text_words	submission_text	submission_words	urls
54	t1_gho9bpc	/r/CelebBattles/comments/ko1duk/marvel_vs_dc_t...	Fuck a nd I thought I didn't have a life LOL	t1_gho5bap	r/CelebBattles	Beav365	2021-01-01 03:57:09	Positive	Negative	-2.0	comment	comment	11	marvel_vs_dc_team_marvel_evangeline_lilly_brie	8	[]
88	t1_ghoms5q	/r/redditmoment/comments/ko1xfy/delete_tik_tok...	Shut the fuck up about this stealing your priv...	t1_ghoe5zj	r/redditmoment	big-shaq-skrra	2021-01-01 06:54:36	Negative	Negative	56.0	comment	comment	28	delete_tik_tok_or_slap_amber_heard	7	[]
105	t1_ghooy78	/r/redditmoment/comments/ko1xfy/delete_tik_tok...	What the fuck	t1_ghon57f	r/redditmoment	Bombz_Armed	2021-01-01 07:28:39	Negative	Negative	55.0	comment	comment	3	delete_tik_tok_or_slap_amber_heard	7	[]
110	t1_ghopc1o	/r/Teenager/comments/knh0wt/just_cuz_i_wanna_k...	who the fuck voted ofr amber heard	t3_knh0wt	r/Teenager	matthew35433ma	2021-01-01 07:34:53	Negative	Negative	1.0	submission	comment	7	just_cuz_i_wanna_know_how_many_of_you_support	10	[]
142	t1_ghot913	/r/redditmoment/comments/ko1xfy/delete_tik_tok...	I'd fuck her for sure	t3_ko1xfy	r/redditmoment	FatHandNoticer	2021-01-01 08:45:21	Positive	Neutral	-11.0	submission	comment	5	delete_tik_tok_or_slap_amber_heard	7	[]

	child_id	permalink	text	parent_id	subreddit	author	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	text_words	submission_text	submission_words	urls
684	t3_kpbsbr	/r/Animemes/comments/kpbsbr/fuck_amber_heard/	Fuck amber heard	NaN	r/Animemes	dingusbob69	2021-01-03 02:38:42	Negative	Negative	269.0	NaN	submission	3	fuck_amber_heard	3	[]
889	t3_kq9lal	/r/memes/comments/kq9lal/fuck_amber_heard/	fuck Amber Heard	NaN	r/memes	guyinAmerica1	2021-01-04 14:18:40	Negative	Negative	1.0	NaN	submission	3	fuck_amber_heard	3	[]
1404	t3_kriljq	/r/JusticeForJohnnyDepp/comments/kriljq/fuck_amber_heard/	fuck amber heard	NaN	r/JusticeForJohnnyDepp	isaac0304	2021-01-06 06:59:05	Negative	Negative	33.0	NaN	submission	3	fuck_amber_heard	3	[]
1853	t3_ktkm8u	/r/EntitledBitch/comments/ktkm8u/fuck_amber_heard/	Fuck amber heard	NaN	r/EntitledBitch	big_pog_human2478	2021-01-09 05:17:34	Negative	Negative	14738.0	NaN	submission	3	fuck_amber_heard	3	[]
2389	t3_kukxmy	/r/SupportAmberHeard/comments/kukxmy/fuck_amber_heard/	FUCK AMBER HEARD	NaN	r/SupportAmberHeard	Flerp6969	2021-01-10 19:28:00	Negative	Negative	54.0	NaN	submission	3	fuck_amber_heard	3	[]

	child_id	permalink	text	parent_id	subreddit	author	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	text_words	submission_text	submission_words	urls
183	t3_ko9ymn	/r/memes/comments/ko9ymn/fuck_amber_heard_credit_to_uwhitewolf699420_for/	Fuck Amber Heard (credit to u/Whitewolf699420 for the template)	NaN	r/memes	-banned-	2021-01-01 11:39:40	Negative	Negative	1.0	NaN	submission	9	fuck_amber_heard_credit_to_uwhitewolf699420_for	7	[]
186	t3_ko9zmj	/r/dankmemes/comments/ko9zmj/fuck_amber_heard_credit_to_uwhitewolf699420_for/	Fuck Amber Heard (credit to u/Whitewolf699420 for template)	NaN	r/dankmemes	-banned-	2021-01-01 11:41:46	Negative	Negative	1.0	NaN	submission	8	fuck_amber_heard_credit_to_uwhitewolf699420_for	7	[]
684	t3_kpbsbr	/r/Animemes/comments/kpbsbr/fuck_amber_heard/	Fuck amber heard	NaN	r/Animemes	dingusbob69	2021-01-03 02:38:42	Negative	Negative	269.0	NaN	submission	3	fuck_amber_heard	3	[]
889	t3_kq9lal	/r/memes/comments/kq9lal/fuck_amber_heard/	fuck Amber Heard	NaN	r/memes	guyinAmerica1	2021-01-04 14:18:40	Negative	Negative	1.0	NaN	submission	3	fuck_amber_heard	3	[]
1404	t3_kriljq	/r/JusticeForJohnnyDepp/comments/kriljq/fuck_amber_heard/	fuck amber heard	NaN	r/JusticeForJohnnyDepp	isaac0304	2021-01-06 06:59:05	Negative	Negative	33.0	NaN	submission	3	fuck_amber_heard	3	[]

	child_id	permalink	text	parent_id	subreddit	author	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	text_words	submission_text	submission_words	urls
13069	t1_guu4ilo	/r/entertainment/comments/msgoz8/johnny_depp_r...	She was feeding him Xanax constantly too, you ...	t1_gutc2jp	r/entertainment	Jreal22	2021-04-17 11:43:17	Negative	Neutral	60.0	comment	comment	76	johnny_depp_releases_lapd_bodycam_footage_proving	7	[]
13073	t1_guu4sfw	/r/entertainment/comments/msgoz8/johnny_depp_r...	Lots of people have their lines fed to them, i...	t1_gutd28y	r/entertainment	Jreal22	2021-04-17 11:45:13	Negative	Negative	6.0	comment	comment	49	johnny_depp_releases_lapd_bodycam_footage_proving	7	[]
13076	t1_guu57ix	/r/entertainment/comments/msgoz8/johnny_depp_r...	Yeah, I couldn't believe this.\n\nThey tried t...	t1_guthn8f	r/entertainment	Jreal22	2021-04-17 11:48:22	Positive	Neutral	23.0	comment	comment	58	johnny_depp_releases_lapd_bodycam_footage_proving	7	[]
13081	t1_guu60bw	/r/entertainment/comments/msgoz8/johnny_depp_r...	Yeah, Depp needed to stop doing the same movie...	t1_guteoj8	r/entertainment	Jreal22	2021-04-17 11:54:07	Positive	Positive	1.0	comment	comment	36	johnny_depp_releases_lapd_bodycam_footage_proving	7	[]
13092	t1_guu7wa8	/r/entertainment/comments/msgoz8/johnny_depp_r...	Jay Z should have taken Chris brown out back a...	t1_gutexk2	r/entertainment	Jreal22	2021-04-17 12:07:33	Negative	Neutral	8.0	comment	comment	39	johnny_depp_releases_lapd_bodycam_footage_proving	7	[]

	parent_id	submission_text	same_count
1191	t3_msgoz8	johnny_depp_releases_lapd_bodycam_footage_proving	29
1114	t3_lu5055	amber_heard_has_been_fired_from_jason_momoas	26
1119	t3_lx2s7w	its_disgusting_that_people_are_less_angry_about	26
1246	t3_n9lwnf	amber_heard_still_in_aquaman_2_despite_proof_that	25
1011	t3_kyj460	amber_heard_is_a_monster_a_gold_digger_looking	18
...	...	...	...
1026	t3_l1qwsh	my_top_6random_order_natalie_portman_vs_emily	1
1027	t3_l26bip	amber_heard_emma_stone_kristen_stewart_and_mary	1
1028	t3_l4tdil	amber_heard_vs_bar_refaeli	1
1030	t3_l6xniu	meta_remember_the_amber_heard_situation_on_this	1
1031	t3_l7a0hx	fuck_amber_heard_but_this_really_made_me_cringe	1

	index	author
0	-banned-	1587
1	AutoModerator	515
2	CelebBattleVoteBot	163
3	LoveAmberHeard42286	124
4	charliedba	99
5	Stanley_Elkind	44
6	Truthbetheprejudice	43
7	gaul66	37
8	sadwook	32
9	Beatplayer	32

	child_id	permalink	text	parent_id	subreddit	author	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	text_words	submission_text	submission_words	urls	urls_count
0	t1_gho6m2p	/r/AskReddit/comments/ko448x/i_strongly_enjoy_...	**PLEASE READ THIS MESSAGE IN ITS ENTIRETY BEF...	t3_ko448x	r/AskReddit	AutoModerator	2021-01-01 03:25:15	Positive	Positive	1.0	submission	comment	155	i_strongly_enjoy_amber_heard_as_a_person_and	9	['http://www.reddit.com']	1
1	t1_gho6wxk	/r/AskReddit/comments/ko45ym/imagine_a_person_...	**PLEASE READ THIS MESSAGE IN ITS ENTIRETY BEF...	t3_ko45ym	r/AskReddit	AutoModerator	2021-01-01 03:28:45	Positive	Positive	1.0	submission	comment	155	imagine_a_person_like_myself_that_strongly_enjoys	8	['http://www.reddit.com']	1
2	t1_ghogbmf	/r/memes/comments/ko5qc9/apkngxpraw_now_theres...	Hello /u/thatgamerguy567! Unfortunately, your ...	t3_ko5qc9	r/memes	AutoModerator	2021-01-01 05:25:21	Positive	Neutral	1.0	submission	comment	239	apkngxpraw_now_theres_the_meeting_code_do_what	8	[]	0
3	t1_ghoh7cn	/r/memes/comments/ko5wmd/apkngxpraw_now_theres...	Rule 9 overused. No Johny Depp or Amber Heard ...	t3_ko5wmd	r/memes	AutoModerator	2021-01-01 05:36:52	Positive	Neutral	1.0	submission	comment	37	apkngxpraw_now_theres_the_meeting_code_do_what	8	[]	0
4	t1_ghoh8an	/r/memes/comments/ko5ws6/apkngxpraw_now_theres...	Rule 9 overused. No Johny Depp or Amber Heard ...	t3_ko5ws6	r/memes	AutoModerator	2021-01-01 05:37:14	Positive	Neutral	1.0	submission	comment	37	apkngxpraw_now_theres_the_meeting_code_do_what	8	[]	0

	child_id	permalink	text	parent_id	subreddit	author	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	text_words	submission_text	submission_words	urls	urls_count
0	t3_ko1ew0	/r/CelebbattlePolls/comments/ko1ew0/marvel_vs_...	Marvel vs DC : Team Marvel (Evangeline Lilly B...	NaN	r/CelebbattlePolls	CelebBattleVoteBot	2021-01-01 00:23:29	Neutral	Positive	1.0	NaN	submission	25	marvel_vs_dc_team_marvel_evangeline_lilly_brie	8	[]	0
1	t1_ghnp3w4	/r/CelebbattlePolls/comments/ko1ew0/marvel_vs_...	Poll for [Marvel vs DC : Team Marvel (Evangeli...	t3_ko1ew0	r/CelebbattlePolls	CelebBattleVoteBot	2021-01-01 00:23:30	Neutral	Positive	1.0	submission	comment	29	marvel_vs_dc_team_marvel_evangeline_lilly_brie	8	['https://reddit.com']	1
2	t1_ghnp3wq	/r/CelebBattles/comments/ko1duk/marvel_vs_dc_t...	Vote here: https://www.reddit.com/poll/ko1ew0\...	t3_ko1duk	r/CelebBattles	CelebBattleVoteBot	2021-01-01 00:23:30	Positive	Neutral	1.0	submission	comment	12	marvel_vs_dc_team_marvel_evangeline_lilly_brie	8	['https://www.reddit.com']	1
3	t3_kp2soe	/r/CelebbattlePolls/comments/kp2soe/rachel_mca...	Rachel McAdams vs Amber Heard	NaN	r/CelebbattlePolls	CelebBattleVoteBot	2021-01-02 18:35:07	Neutral	Neutral	2.0	NaN	submission	5	rachel_mcadams_vs_amber_heard	5	[]	0
4	t1_ghul208	/r/CelebbattlePolls/comments/kp2soe/rachel_mca...	Poll for [Rachel McAdams vs Amber Heard](https...	t3_kp2soe	r/CelebbattlePolls	CelebBattleVoteBot	2021-01-02 18:35:07	Neutral	Neutral	1.0	submission	comment	9	rachel_mcadams_vs_amber_heard	5	['https://reddit.com']	1

	child_id	permalink	text	parent_id	subreddit	author	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	text_words	submission_text	submission_words	urls
0	t3_ms8xkq	/r/Amber_Heard/comments/ms8xkq/long_legged_bea...	Long legged beauty [Amber Heard]	NaN	r/Amber_Heard	charliedba	2021-04-16 18:07:26	Negative	Positive	1.0	NaN	submission	5	Amber_Heard	2	[]
1	t3_msbo0x	/r/BeautifulFemales/comments/msbo0x/amber_hear...	Amber Heard [irtr]	NaN	r/BeautifulFemales	charliedba	2021-04-16 20:23:46	Neutral	Neutral	29.0	NaN	submission	3	amber_heard_irtr	3	[]
2	t3_mtkdvb	/r/Amber_Heard/comments/mtkdvb/angelic_smile_a...	Angelic smile [Amber Heard]	NaN	r/Amber_Heard	charliedba	2021-04-18 20:08:57	Positive	Positive	1.0	NaN	submission	4	Amber_Heard	2	[]
3	t3_mtkfg1	/r/UHQcelebs/comments/mtkfg1/amber_heard_2100_...	Amber Heard [2100 x 3150]	NaN	r/UHQcelebs	charliedba	2021-04-18 20:11:13	Neutral	Neutral	55.0	NaN	submission	5	amber_heard_2100_x_3150	5	[]
4	t3_mtkgku	/r/FamousFaces/comments/mtkgku/amber_heard_210...	Amber Heard [2100 x 3150]	NaN	r/FamousFaces	charliedba	2021-04-18 20:12:54	Neutral	Neutral	5.0	NaN	submission	5	amber_heard_2100_x_3150	5	[]

	index	sentiment_nltk	sentiment_blob
0	Neutral	10371	7034
1	Negative	3994	4565
2	Positive	3940	6706

	child_id	permalink	text	parent_id	subreddit	author	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	text_words	submission_text	submission_words	urls
0	t1_gmzc8ly	/r/JerkOffToCelebs/comments/lhu3nv/amber_heard...	I would put it in every one of her holes	t3_lhu3nv	r/JerkOffToCelebs	Stanley_Elkind	2021-02-11 20:42:48	Neutral	Neutral	3.0	submission	comment	10	amber_heard	2	[]
1	t1_gnf1pv3	/r/DC_Cinematic/comments/ljqbut/discussion_hon...	Everyone is cool burgeoning cult leader Jared ...	t3_ljqbut	r/DC_Cinematic	Stanley_Elkind	2021-02-14 15:28:05	Positive	Positive	1.0	submission	comment	9	DC_Cinematic	2	[]
2	t1_gni2nrr	/r/JerkOffToCelebs/comments/lk5bc8/cant_help_b...	Black widow	t3_lk5bc8	r/JerkOffToCelebs	Stanley_Elkind	2021-02-15 04:21:15	Negative	Neutral	1.0	submission	comment	2	cant_help_but_notice_the_way_she_looks_at_you_now	11	[]
3	t1_gnuualx	/r/JerkOffToCelebs/comments/lmc28b/amber_heard...	The more people hate her the more I want to fu...	t3_lmc28b	r/JerkOffToCelebs	Stanley_Elkind	2021-02-18 05:36:20	Negative	Neutral	3.0	submission	comment	12	amber_heard_could_do_with_a_skull_fucking	8	[]
4	t1_go1jx5r	/r/JerkOffToCelebs/comments/lnodlf/amber_heard...	I can’t be quit her. I don’t want to.	t3_lnodlf	r/JerkOffToCelebs	Stanley_Elkind	2021-02-19 20:01:40	Neutral	Positive	1.0	submission	comment	9	amber_heard_is_the_sexiest_psycho_i_ever_saw	9	[]

	child_id	permalink	text	parent_id	subreddit	author	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	text_words	submission_text	submission_words	urls
0	t3_mtioam	/r/MensRights/comments/mtioam/johnny_depp_seen...	Johnny Depp Seen In Rare Photos In Spain, As H...	NaN	r/MensRights	Truthbetheprejudice	2021-04-18 18:41:42	Positive	Neutral	1.0	NaN	submission	17	johnny_depp_seen_in_rare_photos_in_spain_as_his	10	[]
1	t3_mtjtsr	/r/movies/comments/mtjtsr/johnny_depp_makes_an...	Johnny Depp Makes an Appearance in Spain as La...	NaN	r/movies	Truthbetheprejudice	2021-04-18 19:40:25	Positive	Negative	1.0	NaN	submission	16	johnny_depp_makes_an_appearance_in_spain_as	8	[]
2	t3_mtkogv	/r/entertainment/comments/mtkogv/johnny_depp_m...	Johnny Depp Makes an Appearance in Spain as La...	NaN	r/entertainment	Truthbetheprejudice	2021-04-18 20:24:14	Positive	Negative	1.0	NaN	submission	16	johnny_depp_makes_an_appearance_in_spain_as	8	[]
3	t3_muyp3k	/r/MensRights/comments/muyp3k/justiceforjohnny...	#JusticeForJohnnyDepp Johnny Depp fans donate ...	NaN	r/MensRights	Truthbetheprejudice	2021-04-20 19:47:45	Neutral	Negative	75.0	NaN	submission	12	justiceforjohnnydepp_johnny_depp_fans_donate_40k	6	[]
4	t3_muypet	/r/entertainment/comments/muypet/justiceforjoh...	#JusticeForJohnnyDepp Johnny Depp fans donate ...	NaN	r/entertainment	Truthbetheprejudice	2021-04-20 19:48:06	Neutral	Negative	1.0	NaN	submission	12	justiceforjohnnydepp_johnny_depp_fans_donate_40k	6	[]

	child_id	permalink	text	parent_id	subreddit	author	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	text_words	submission_text	submission_words	urls
0	t1_ghobzuy	/r/CelebBattles/comments/ko1duk/marvel_vs_dc_t...	Marvel	t3_ko1duk	r/CelebBattles	gaul66	2021-01-01 04:29:09	Neutral	Positive	8.0	submission	comment	1	marvel_vs_dc_team_marvel_evangeline_lilly_brie	8	[]
1	t1_ghuwup4	/r/CelebBattles/comments/kp2qmx/rachel_mcadams...	Rachel	t3_kp2qmx	r/CelebBattles	gaul66	2021-01-02 20:16:31	Neutral	Neutral	2.0	submission	comment	1	rachel_mcadams_vs_amber_heard	5	[]
2	t1_giqmms5	/r/CelebBattles/comments/ku6peu/hotter_one_gal...	Gal	t3_ku6peu	r/CelebBattles	gaul66	2021-01-10 06:10:19	Neutral	Neutral	5.0	submission	comment	1	hotter_one_gal_gadot_vs_amber_heard_vs_alison_...	10	[]
3	t1_gk1f28y	/r/CelebBattles/comments/l1qwsh/my_top_6random...	Natalie	t3_l1qwsh	r/CelebBattles	gaul66	2021-01-21 07:10:16	Neutral	Neutral	1.0	submission	comment	1	my_top_6random_order_natalie_portman_vs_emily	8	[]
4	t1_gkf1t2n	/r/CelebBattles/comments/l3eo1l/katy_perry_vs_...	Evangeline	t3_l3eo1l	r/CelebBattles	gaul66	2021-01-23 18:03:01	Neutral	Neutral	2.0	submission	comment	1	katy_perry_vs_evangeline_lilly_vs_amber_heard	8	[]

	child_id	permalink	text	parent_id	subreddit	author	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	text_words	submission_text	submission_words	urls
0	t1_gxpnlsu	/r/pussypassdenied/comments/n95rfx/amber_heard...	Good... \n\n"" He said: “The severity of these...	t3_n95rfx	r/pussypassdenied	sadwook	2021-05-11 10:09:06	Positive	Negative	1.0	submission	comment	101	amber_heard_to_struggle_in_new_lapd_domestic	8	[]
1	t1_gxpnp4c	/r/pussypassdenied/comments/n95rfx/amber_heard...	i will not hold back. JUSTICE FOR JOHNNY	t3_n95rfx	r/pussypassdenied	sadwook	2021-05-11 10:10:29	Neutral	Negative	2.0	submission	comment	8	amber_heard_to_struggle_in_new_lapd_domestic	8	[]
2	t1_gxpntzg	/r/pussypassdenied/comments/n95rfx/amber_heard...	we wont stay silent. this mans reputation is g...	t1_gxnoecm	r/pussypassdenied	sadwook	2021-05-11 10:12:32	Positive	Neutral	1.0	comment	comment	34	amber_heard_to_struggle_in_new_lapd_domestic	8	[]
3	t1_gxpnxf1	/r/pussypassdenied/comments/n95rfx/amber_heard...	pretty much the summary of her career. im not ...	t1_gxoh4o0	r/pussypassdenied	sadwook	2021-05-11 10:13:55	Negative	Neutral	3.0	comment	comment	24	amber_heard_to_struggle_in_new_lapd_domestic	8	[]
4	t1_gxpnzbf	/r/pussypassdenied/comments/n95rfx/amber_heard...	bruh	t1_gxn7p1k	r/pussypassdenied	sadwook	2021-05-11 10:14:43	Neutral	Neutral	0.0	comment	comment	1	amber_heard_to_struggle_in_new_lapd_domestic	8	[]

	child_id	permalink	text	parent_id	subreddit	author	created_at	sentiment_blob	sentiment_nltk	score	top_level	submission_comment	text_words	submission_text	submission_words	urls
0	t1_gpl1ocz	/r/TrueOffMyChest/comments/lx2s7w/its_disgusti...	Wait wait. Has something changed here. Because...	t3_lx2s7w	r/TrueOffMyChest	Beatplayer	2021-03-03 22:00:23	Negative	Neutral	36.0	submission	comment	36	its_disgusting_that_people_are_less_angry_about	8	[]
1	t1_gpl5mw9	/r/TrueOffMyChest/comments/lx2s7w/its_disgusti...	But then again, that was just a statement made...	t1_gpl5997	r/TrueOffMyChest	Beatplayer	2021-03-03 22:30:38	Positive	Neutral	-1.0	comment	comment	86	its_disgusting_that_people_are_less_angry_about	8	[]
2	t1_gpl71q8	/r/TrueOffMyChest/comments/lx2s7w/its_disgusti...	Like most abusive relationships tbh, from the ...	t1_gpl6uww	r/TrueOffMyChest	Beatplayer	2021-03-03 22:41:44	Positive	Neutral	-2.0	comment	comment	26	its_disgusting_that_people_are_less_angry_about	8	[]
3	t1_gpla7k5	/r/TrueOffMyChest/comments/lx2s7w/its_disgusti...	K. So I’m 12.5 mins in, and absolutely shatter...	t1_gpl8d57	r/TrueOffMyChest	Beatplayer	2021-03-03 23:07:01	Negative	Negative	20.0	comment	comment	109	its_disgusting_that_people_are_less_angry_about	8	[]
4	t1_gplbi77	/r/TrueOffMyChest/comments/lx2s7w/its_disgusti...	I’m not sure that the case to be honest. \n\nI...	t1_gpl9u5x	r/TrueOffMyChest	Beatplayer	2021-03-03 23:17:45	Negative	Neutral	15.0	comment	comment	149	its_disgusting_that_people_are_less_angry_about	8	[]

	author
-banned-	60
Truthbetheprejudice	5
bradje61	3
Sleeper1034	3
krttz35	3
DerpimusNoobimus	2
Memestrats4life	2
shalanarose	2
MikiSayaka33	2
spiritual_one1	2

	user_name	has_verified_email	is_mod	is_gold	is_banned	comment_karma	link_karma	user_created_at	banned_unverified	creation_year
4432	AutoModerator	True	True	True	False	1000.0	1000.0	2012-01-05 05:24:28	others	others
6953	DrewFlan	True	False	False	False	325044.0	5513.0	2012-08-27 19:39:06	others	others

	child_id	permalink	text	parent_id	subreddit	created_at	sentiment_blob	sentiment_nltk	score	top_level	...	is_mod	is_gold	is_banned	comment_karma	link_karma	user_created_at	banned_unverified	creation_year	diff	days_after_creation
2784	t1_ghra7an	/r/Makeup/comments/kogfek/amber_heard_the_face...	Um... wtf. A UK court found that Johnny did ph...	t3_kogfek	r/Makeup	2021-01-01 20:39:19	Negative	Negative	113.0	submission	...	False	False	False	13294.0	3484.0	2020-12-25 03:08:50	unverified	2020	7 days 17:30:29	7.0
2785	t1_ghrcksp	/r/Makeup/comments/kogfek/amber_heard_the_face...	I figure it’s just Pirates of the Caribbean st...	t1_ghrbajl	r/Makeup	2021-01-01 21:00:15	Negative	Negative	48.0	comment	...	False	False	False	13294.0	3484.0	2020-12-25 03:08:50	unverified	2020	7 days 17:51:25	7.0
2904	t1_ghse9qw	/r/Vent/comments/jpgsyv/i_fucking_hate_amber_h...	JOHNNY DEPP IS A SCUMBAG DRUG ADDICT PIECE OF ...	t3_jpgsyv	r/Vent	2021-01-02 02:43:56	Negative	Negative	1.0	submission	...	False	False	False	188.0	20.0	2020-12-28 04:17:41	others	2020	4 days 22:26:15	4.0
2905	t1_ghsebqn	/r/Vent/comments/jpgsyv/i_fucking_hate_amber_h...	GO FUCK YOURSELF	t1_gbf65ke	r/Vent	2021-01-02 02:44:27	Negative	Negative	0.0	comment	...	False	False	False	188.0	20.0	2020-12-28 04:17:41	others	2020	4 days 22:26:46	4.0
2906	t1_ghsen7o	/r/Vent/comments/jpgsyv/i_fucking_hate_amber_h...	FUCK YOU AND FUCK JOHNNY DEPP	t1_gbhcttr	r/Vent	2021-01-02 02:47:19	Negative	Negative	-1.0	comment	...	False	False	False	188.0	20.0	2020-12-28 04:17:41	others	2020	4 days 22:29:38	4.0

Case Study: Reddit Social Network Analysis Against Influence Operation¶

Table of Contents¶

Introduction¶

Exploratory Data Analysis¶

Reddit Comments/Submission Data¶

Reddit Comments¶

Reddit Submissions¶

The number of negative parent comments on submissions¶

1- The # of submissions VS the # of comments¶

2- NLTK vs BLOB clssification¶

Common results¶

3- Investigate the text column¶

Investigate The Most Negative key Words Used (from the wordcloud map)¶

Lets first check for the users using the word 'f*ck'¶

Jreal22¶

Check for the repeated text¶

Negative class text¶

5- Investigate the Negative Submissions Text¶

7- Check the number of negative text words

8- Most commented user¶

Further Investigate The Most Commented Users In 2021¶

AutoModerator¶

CelebBattleVoteBot¶

charliedba¶

Stanley_Elkind¶

Truthbetheprejudice¶

gaul66¶

sadwook¶

Beatplayer¶

9- Invesigating authors with the most negative comments / submissions¶

10- Invesigating authors with the most negative submissions¶

Let us first Investigate authors with the most submissions¶

11- Check wether the users contributing the most to negative comments/submissions are mod, gold or having a verified email¶

13- Invesigating subreddits with the most negative comments¶

14- Submission URLS¶

16- Invesigating submissions with the most negative comments¶

look at number 4 and 5 as they are more accurate¶

Merged Users Data with Comments & Submissions Data¶

Difference in time between creating the account and posting (negative)¶

Posting Duration After Account Creation¶

Estimation of Number of User Accounts Created in each year / having negative contributions in 2021¶

Peak Days¶

Which dates has the highest negative contrbitions for users?¶

Conclusions¶